Validation loss vs validation metric in Keras

问题:

I have written a model in Keras (with theano backend) and compile my model like this: model.compile(Adam(0.001), loss='mse', metrics=['mse', 'mae']), i.e. my objective loss function is mean squared error and the metrics to report are mean squared error and mean absolute error.

Then I run my model: model.fit(X_train, y_train, nb_epoch=500, validation_data=(X_test, y_test))

Keras reports results as:

Epoch 500/500: 0s - loss: 5.5990 - mean_squared_error: 4.4311 - mean_absolute_error: 0.9511 - val_loss: 7.5573 - val_mean_squared_error: 6.3877 - val_mean_absolute_error: 1.1335

I expected val_loss to be same as val_mean_squared_error. What is val_loss here if not val_mean_squared_error?

文章来源: Validation loss vs validation metric in Keras